Attention Deficit Hyperactivity Disorder Detection Using Deep Learning Approach

dc.contributor.author	Hamim, Md Abrar
dc.contributor.author	Tanmoy, F.M.
dc.contributor.author	Tasfia, Orin
dc.contributor.author	Juthi, Farzana Alam
dc.date.accessioned	2024-04-28T10:10:26Z
dc.date.available	2024-04-28T10:10:26Z
dc.date.issued	2023-11-23
dc.identifier.uri	http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/12192
dc.description.abstract	ADHD, a neurodevelopmental disorder characterized by hyperactivity, inattention, and impulsivity, has many detrimental impacts and is out of proportion to age. ADHD causes executive failure and emotional instability, which can lower academic performance. We propose a machine learning and artificial intelligence-driven approach to diagnose and early detect this disease and assist ADHD medicine. SVM, logistic regression, XGBoost, AdaBoost, and two deep learning models were applied to our dataset (ANN and CNN). Our ANN model had 99% accuracy in dependability, expandability, and generalizability. We plan to use our machine learning technology to enhance ADHD diagnosis and treatment for everyone.	en_US
dc.language.iso	en_US	en_US
dc.publisher	IEEE	en_US
dc.subject	Deep learning	en_US
dc.subject	Deficit hyperactivity	en_US
dc.subject	Disorder detection	en_US
dc.title	Attention Deficit Hyperactivity Disorder Detection Using Deep Learning Approach	en_US
dc.type	Article	en_US